POST-TENSIONING OF TIMBER BEAMS WITH BASALT FIBRE REINFORCED POLYMER

Improvements in the structural performance of glulam timber beams by the inclusion of reinforcing materials can improve both the service performance and ultimate capacity. In recent years research focusing on the addition of fibre reinforced polymers to strengthen members has yielded positive results. However, the FRP material is still a relatively expensive material and its full potential has not been realised in combination with structural timber. This paper describes a series of four-point bending tests that were conducted, under service and ultimate loads, on post-tensioned glulam timber beams where the reinforcing tendon used was 12 mm diameter Basalt Fibre Reinforced Polymer (BFRP). The research was designed to evaluate the additional benefits of including an active type of reinforcement, by post-tensioning the BFRP tendon, as opposed to the passive approach of simply reinforcing the timber beam.
From the laboratory investigations, it was established that there was a 16% increase in load carrying capacity, in addition to a 14% reduction in deflection under service loads when members containing the post-tensioned BFRP composite are compared with control timber specimens. Additionally a more favourable ductile failure mode was witnessed compared to the brittle failure of an unreinforced timber beam. The results support the assumption that by initially stressing the embedded FRP tendon the structural benefits experienced by the timber member increase in a number of ways, indicating that there is significant scope for this approach in practical applications.

Development of a Bridge Weigh-in-Motion Sensor: Performance comparison using fibre optic and electric resistance strain sensor systems

This paper addresses the problems of effective in situ measurement of the real-time strain for bridge weigh in motion in reinforced concrete bridge structures through the use of optical fiber sensor systems. By undertaking a series of tests, coupled with dynamic loading, the performance of fiber Bragg grating-based sensor systems with various amplification techniques were investigated. In recent years, structural health monitoring (SHM) systems have been developed to monitor bridge deterioration, to assess load levels and hence extend bridge life and safety. Conventional SHM systems, based on measuring strain, can be used to improve knowledge of the bridge's capacity to resist loads but generally give no information on the causes of any increase in stresses. Therefore, it is necessary to find accurate sensors capable of capturing peak strains under dynamic load and suitable methods for attaching these strain sensors to existing and new bridge structures. Additionally, it is important to ensure accurate strain transfer between concrete and steel, adhesives layer, and strain sensor. The results show the benefits in the use of optical fiber networks under these circumstances and their ability to deliver data when conventional sensors cannot capture accurate strains and/or peak strains.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Development and evaluation of a novel integrated anti-icing/de-icing technology for carbon fibre composite aerostructures using an electro-conductive textile

The preliminary evaluation is described of a new electro-thermal anti-icing/de-icing device for carbon fibre composite aerostructures. The heating element is an electro-conductive carbon-based textile (ECT) by Gorix. Electrical shorting between the structural carbon fibres and the ECT was mitigated by incorporating an insulating layer formed of glass fibre plies or a polymer film. A laboratory-based anti-icing and de-icing test program demonstrated that the film-insulated devices yielded better performance than the glssass fibre insulated ones. The heating capability after impact damage was maintained as long as the ECT fabric was not breached to the extent of causing electrical shorting. A modified structural scarf repair was shown to restore the heating capacity of a damaged specimen.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Hair and hair style in prehistoric Malta

Paper focusing on the use and significance of hair and hair style in ancient societies